Prostate cancer (PCA) is the second leading cause of cancer-related deaths in US men. Although PCA screening is controversial for the average risk male population, a subset of men who die of PCA have early onset disease. Many of these men are at increased risk due to a familial predisposition or due to African American ethnicity. Recently, several genetic markers have been reported to be informative for early onset PCA which need validation and clinical study to understand their utility in PCA risk assessment, particularly for AA men. Objective/Hypothesis: The hypothesis is that early onset genetic markers have variable strength of association to PCA and lend unique information for clinical PCA risk assessment in diverse populations.
Aim 1 : Evaluate the strength of association to PCA by early onset genetic markers. Genetic markers for study are rs6983561, rs4430796, rs266849, rs10993994, rs1799950, and rs2171492. The SCORE (Study of Clinical Outcomes, Risk, and Ethnicity) case-control study at the University of Pennsylvania will be utilized and has 1692 cases and 932 controls. Genotyping methods will include Taqman(R) SNP Genotyping Assays (Applied Biosystems), pyrosequencing, or standard sequencing. Statistical methods will include multinomial logistic regressions. Subset analyses will be conducted to analyze if early onset genetic markers are associated with PCA in men diagnosed at age <55 years.
Aim 2 : Clinically characterize early onset genetic markers in a longitudinal screening cohort of ethnically-diverse, young, high-risk men in the Prostate Cancer Risk Assessment Program (PRAP). Currently PRAP has over 750 participants and 10% have been diagnosed with PCA. Mean age of participants is 44 years. Genetic markers and genotyping are identical to Aim 1. Time to PCA diagnosis and longitudinal prediction for PCA by the baseline PSA will be evaluated at several follow-up intervals. Statistical methods will include Cox proportional hazards regressions.
Aim 3 : Validate early onset genetic markers associated with PCA using the risk prediction calculator from the Prostate Cancer Prevention Trial (PCPT). We will compare the area under the ROC curve (AUC) for the PCPT risk calculator alone and in combination with early onset genetic markers. Genetic association to aggressive PCA will be validated by the ability to predict PCPT risk scores for high-grade disease in the PRAP cohort. Significance: This study will make significant advances toward understanding the magnitude of association to PCA (and aggressive PCA) and clinical use of early onset genetic markers. Men carrying early onset markers associated with aggressive PCA or predicting early time to diagnosis of PCA can be recommended for tailored screening strategies while men not carrying these markers can be spared unnecessary morbidity. In this way, we will be personalizing PCA risk assessment using genetics.

Public Health Relevance

Prostate cancer early detection needs to become a personalized process by the incorporation of genetic markers to risk stratify men. A subset of men are at risk of dying of prostate cancer at a young age, particularly African American men and men with a family history of prostate cancer. Upon further study as proposed here, genetic markers associated with early onset prostate cancer can clinically be useful to counsel high-risk men regarding tailored early detection strategies and spare other men unnecessary morbidity and therefore truly personalize prostate cancer risk assessment.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Small Research Grants (R03)
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Special Emphasis Panel (ZCA1-SRLB-F (J1))
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Kagan, Jacob
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Fox Chase Cancer Center
United States
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Handorf, Elizabeth; Crumpler, Nicole; Gross, Laura et al. (2014) Prevalence of the HOXB13 G84E mutation among unaffected men with a family history of prostate cancer. J Genet Couns 23:371-6
Hughes, Lucinda; Zhu, Fang; Ross, Eric et al. (2012) Assessing the clinical role of genetic markers of early-onset prostate cancer among high-risk men enrolled in prostate cancer early detection. Cancer Epidemiol Biomarkers Prev 21:53-60